Spatial social networks identified from urban group travel

TL;DR

This study analyzes urban group travel using Beijing metro data, distinguishing friend groups from encounters, revealing their network structures, predictability, and potential for optimized traffic management.

Contribution

Introduces a method to identify and analyze group travel and social relationships from urban mobility data, highlighting differences from encounter networks and implications for smart transportation.

Findings

Friend group travels are highly predictable.

Friendship networks have distinct structural properties.

Redistributing group travel time can save approximately 34,190 minutes.

Abstract

While the individual travel implicates the trace of individual mobility decision, group travels signify the possible social relationship behind these traces. Different from online social network, spatial interaction between individuals is a critical yet unknown dimension to understand the collective behaviors in a city. In this paper, based on over 127 million trips in Beijing metro network, we develop a method to distinguish the group travel of friends from the encounter travel of familiar strangers. We find travels of friends are among the most predictable groups. These identified friendships are interwoven and form a friendship network, with structural properties different from encounter network. The topological role of individuals in this network is found strongly correlated with her travel predictability. The overall time savings of about 34190 minutes after redistribution of inefficient group traveler with flexible travel purposes shows the potential of designing specific traffic fares for group travel. Our identification and understanding of group travel may help to develop and organize new traffic mode in the future smart transportation.